Predictive Models Aid Prognostication
Bibliographic record
Abstract
BACKGROUND: In a recent multicenter Canadian study in heart failure (HF), model predictions proved more accurate than physicians. OBJECTIVES: Simulating clinical practice, the authors evaluated the predictive value of combining model predictions with physician estimated 1-year mortality in HF outpatients. METHODS: This post hoc analysis of a Canadian multicenter cohort study included HF outpatients (left ventricular ejection fraction ≤40%). HF cardiologists and family doctors estimated patient 1-year mortality using clinical judgment. The Seattle HF Model (SHFM) predicted mortality. All patients were followed for 1 year to collect mortality. Stratified by specialty, we compared the performance of SHFM and physician estimates alone, with a model integrating physician and SHFM predictions using a random forest survival model, evaluating discrimination (C-statistic), calibration (observed vs predicted event rate), risk reclassification, and clinical net benefit. RESULTS: In 1,643 HF patients, 1-year mortality was 9% (95% CI: 8%-11%). The SHFM had adequate discrimination (C-statistic 0.76; 95% CI: 0.72-0.80) and excellent calibration. Physicians showed adequate discrimination (0.75; 95% CI: 0.71-0.79 for cardiologists; 0.72; 95% CI: 0.66-0.78 for family doctors) and poor calibration with significant risk overestimation. Integrating SHFM and physician predictions, discrimination significantly improved (0.82; 95% CI: 0.78-0.86 for cardiologists; 0.87; 95% CI: 0.83-0.91 for family doctors) with excellent calibration. By risk reclassification, among patients without events, the integrated model better risk-classified 71% (95% CI: 70%-72%) vs cardiologists and 60% (95% CI: 58%-61%) vs family doctors; among patients with events, the model misclassified 45% (95% CI: 58%-63%) vs cardiologists and 11% (95% CI: 25% to 3%) vs family doctors. The integrated model led to higher clinical benefit. CONCLUSIONS: Integrating SHFM predictions with physician judgment improved accuracy. Model-informed assessment provides prognostic accuracy for clinical decision-making. (Predicted Prognosis in Heart Failure Intuition; NCT04009798).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".